
import pandas as pd  # importing Pandas for dataset import
import matplotlib.pyplot as plt  # importing for basic visualization and seaborn
import seaborn as sns  # importing for better visualization features
import warnings  # importing to suppress the warnings of deprecated features
import numpy as np  # importing numpy

warnings.filterwarnings("ignore")  # suppress the warnings
# for inline plotting
%matplotlib inline
pd.set_option(
    "display.float_format", lambda x: "%.5f" % x
)  # to suppress scientific notations
# Removes the limit for the number of displayed columns
pd.set_option("display.max_columns", None)
# Sets the limit for the number of displayed rows
pd.set_option("display.max_rows", 200)
# To build linear model for prediction
from sklearn.linear_model import LinearRegression

# To check model performance
from sklearn.model_selection import train_test_split

# Sets the limit for the number of displayed rows
pd.set_option("display.max_rows", 200)
# Libraries to build decision tree classifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import BaggingClassifier, RandomForestClassifier
from sklearn import tree

# To tune different models
from sklearn.model_selection import (
    GridSearchCV,
    train_test_split,
    StratifiedKFold,
    cross_val_score,
)
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import (
    BaggingRegressor,
    RandomForestRegressor,
    GradientBoostingRegressor,
    AdaBoostRegressor,
    StackingRegressor,
)
from xgboost import XGBRegressor, XGBClassifier
from sklearn.ensemble import AdaBoostClassifier, GradientBoostingClassifier

# To be used for missing value imputation
from sklearn.impute import SimpleImputer

# To help with model building
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import (
    AdaBoostClassifier,
    GradientBoostingClassifier,
    RandomForestClassifier,
    BaggingClassifier,
)

# To get different metric scores, and split data
from sklearn import metrics
from sklearn.metrics import (
    f1_score,
    accuracy_score,
    recall_score,
    precision_score,
    confusion_matrix,
    roc_auc_score,
    plot_confusion_matrix,
)

# To be used for data scaling and one hot encoding
from sklearn.preprocessing import StandardScaler, MinMaxScaler, OneHotEncoder

# To be used for tuning the model
from sklearn.model_selection import GridSearchCV, RandomizedSearchCV

# To be used for creating pipelines and personalizing them
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer

# To define maximum number of columns to be displayed in a dataframe
pd.set_option("display.max_columns", None)
# This will help in making the Python code more structured automatically (good coding practice)
#%load_ext nb_black
#!pip install imblearn
from imblearn.over_sampling import SMOTE


# importing dataset stored in csv file and setting 1st cloumn as index
df = pd.read_csv(
    r"C:\Users\user\Desktop\AI_ML_Austin\notebook prog files\ProjectWork5\BankChurners.csv"
)
# creating a copy of the data so that original data remains unchanged
ccpredf1 = df.copy()


ccpredf1.head(10)  # display top 10 rows


np.random.seed(
    1
)  # random set with seed 1, so the same set of random values can be generated everytime
ccpredf1.sample(n=20)  # Print 20 random rows to check the dataset


print(
    f"There are {ccpredf1.shape[0]} rows and {ccpredf1.shape[1]} columns."
)  # f-string

There are 10127 rows and 21 columns.


ccpredf1.dtypes.value_counts()  # Gives count of the different datatype in dataset

int64      10
object      6
float64     5
dtype: int64


ccpredf1.info()  # display complete info

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 10127 entries, 0 to 10126
Data columns (total 21 columns):
 #   Column                    Non-Null Count  Dtype  
---  ------                    --------------  -----  
 0   CLIENTNUM                 10127 non-null  int64  
 1   Attrition_Flag            10127 non-null  object 
 2   Customer_Age              10127 non-null  int64  
 3   Gender                    10127 non-null  object 
 4   Dependent_count           10127 non-null  int64  
 5   Education_Level           8608 non-null   object 
 6   Marital_Status            9378 non-null   object 
 7   Income_Category           10127 non-null  object 
 8   Card_Category             10127 non-null  object 
 9   Months_on_book            10127 non-null  int64  
 10  Total_Relationship_Count  10127 non-null  int64  
 11  Months_Inactive_12_mon    10127 non-null  int64  
 12  Contacts_Count_12_mon     10127 non-null  int64  
 13  Credit_Limit              10127 non-null  float64
 14  Total_Revolving_Bal       10127 non-null  int64  
 15  Avg_Open_To_Buy           10127 non-null  float64
 16  Total_Amt_Chng_Q4_Q1      10127 non-null  float64
 17  Total_Trans_Amt           10127 non-null  int64  
 18  Total_Trans_Ct            10127 non-null  int64  
 19  Total_Ct_Chng_Q4_Q1       10127 non-null  float64
 20  Avg_Utilization_Ratio     10127 non-null  float64
dtypes: float64(5), int64(10), object(6)
memory usage: 1.6+ MB


ccpredf1.isnull().sum()

CLIENTNUM                      0
Attrition_Flag                 0
Customer_Age                   0
Gender                         0
Dependent_count                0
Education_Level             1519
Marital_Status               749
Income_Category                0
Card_Category                  0
Months_on_book                 0
Total_Relationship_Count       0
Months_Inactive_12_mon         0
Contacts_Count_12_mon          0
Credit_Limit                   0
Total_Revolving_Bal            0
Avg_Open_To_Buy                0
Total_Amt_Chng_Q4_Q1           0
Total_Trans_Amt                0
Total_Trans_Ct                 0
Total_Ct_Chng_Q4_Q1            0
Avg_Utilization_Ratio          0
dtype: int64


# let's check for duplicate values in the data
ccpredf1.duplicated().sum()

0


ccpredf1.nunique()

CLIENTNUM                   10127
Attrition_Flag                  2
Customer_Age                   45
Gender                          2
Dependent_count                 6
Education_Level                 6
Marital_Status                  3
Income_Category                 6
Card_Category                   4
Months_on_book                 44
Total_Relationship_Count        6
Months_Inactive_12_mon          7
Contacts_Count_12_mon           7
Credit_Limit                 6205
Total_Revolving_Bal          1974
Avg_Open_To_Buy              6813
Total_Amt_Chng_Q4_Q1         1158
Total_Trans_Amt              5033
Total_Trans_Ct                126
Total_Ct_Chng_Q4_Q1           830
Avg_Utilization_Ratio         964
dtype: int64


ccpredf1.describe(
    include="all"
).T  # describe all dataset. Use transpose so column wise details can be view from top to bottompurpredf1.describe(include="all").T #describe all dataset. Use transpose so column wise details can be view from top to bottom


# function to plot a boxplot and a histogram along the same scale.
def histogram_boxplot(data, feature, figsize=(12, 7), kde=False, bins=None):
    f2, (ax_box2, ax_hist2) = plt.subplots(
        nrows=2,  # Number of rows of the subplot grid= 2
        sharex=True,  # x-axis will be shared among all subplots
        gridspec_kw={"height_ratios": (0.25, 0.75)},
        figsize=figsize,
    )  # creating the 2 subplots
    sns.boxplot(
        data=data, x=feature, ax=ax_box2, showmeans=True, color="violet"
    )  # boxplot will be created and a star will indicate the mean value of the column
    sns.histplot(
        data=data, x=feature, kde=kde, ax=ax_hist2, bins=bins, palette="winter"
    ) if bins else sns.histplot(
        data=data, x=feature, kde=kde, ax=ax_hist2
    )  # For histogram
    ax_hist2.axvline(
        data[feature].mean(), color="green", linestyle="--"
    )  # Add mean to the histogram
    ax_hist2.axvline(
        data[feature].median(), color="black", linestyle="-"
    )  # Add median to the histogram


# function to label barplot
def labeled_barplot(data, feature, perc=False, n=None):
    total = len(data[feature])  # length of the column
    count = data[feature].nunique()
    if n is None:
        plt.figure(figsize=(count + 1, 5))
    else:
        plt.figure(figsize=(n + 1, 5))

    plt.xticks(rotation=90, fontsize=15)
    ax = sns.countplot(
        data=data,
        x=feature,
        palette="Paired",
        order=data[feature].value_counts().index[:n].sort_values(),
    )

    for p in ax.patches:
        if perc == True:
            label = "{:.1f}%".format(
                100 * p.get_height() / total
            )  # percentage of each class of the category
        else:
            label = p.get_height()  # count of each level of the category

        x = p.get_x() + p.get_width() / total  # width of the plot
        y = p.get_y() + p.get_height() + 1.5  # height of the plot

        ax.annotate(label, (x, y), size=12, rotation=0)  # annotate the percentage

    plt.show()  # show the plot


labeled_barplot(ccpredf1, "Attrition_Flag", perc=True)  # barplot


histogram_boxplot(ccpredf1, "Customer_Age")  # boxplot


labeled_barplot(ccpredf1, "Gender", perc=True)  # barplot


labeled_barplot(ccpredf1, "Dependent_count", perc=True)  # barplot


labeled_barplot(ccpredf1, "Education_Level", perc=True)  # barplot


labeled_barplot(ccpredf1, "Marital_Status", perc=True)  # barplot


labeled_barplot(ccpredf1, "Income_Category", perc=True)  # barplot


labeled_barplot(ccpredf1, "Card_Category", perc=True)  # barplot


labeled_barplot(ccpredf1, "Months_on_book", perc=True)  # barplot


labeled_barplot(ccpredf1, "Total_Relationship_Count", perc=True)  # barplot


labeled_barplot(ccpredf1, "Months_Inactive_12_mon", perc=True)  # barplot


labeled_barplot(ccpredf1, "Contacts_Count_12_mon", perc=True)  # barplot


histogram_boxplot(ccpredf1, "Credit_Limit")  # boxplot


sns.pairplot(ccpredf1, hue="Attrition_Flag")
# pair plot multiple pair wise bivariate distributions

<seaborn.axisgrid.PairGrid at 0x1bd26f665e0>


plt.figure(figsize=(10, 5))
sns.boxplot(
    x="Customer_Age", y="Attrition_Flag", data=ccpredf1
)  # plot to assess important variables
plt.show()


plt.figure(figsize=(10, 5))
sns.countplot(x="Attrition_Flag", hue="Gender", data=ccpredf1)
# plot to assess important variables

<AxesSubplot:xlabel='Attrition_Flag', ylabel='count'>


plt.figure(figsize=(10, 5))
sns.countplot(x="Attrition_Flag", hue="Dependent_count", data=ccpredf1)
# plot to assess important variables

<AxesSubplot:xlabel='Attrition_Flag', ylabel='count'>


plt.figure(figsize=(10, 5))
sns.countplot(x="Dependent_count", hue="Attrition_Flag", data=ccpredf1)
# plot to assess important variables

<AxesSubplot:xlabel='Dependent_count', ylabel='count'>


plt.figure(figsize=(10, 5))
sns.countplot(x="Education_Level", hue="Attrition_Flag", data=ccpredf1)
# plot to assess important variables

<AxesSubplot:xlabel='Education_Level', ylabel='count'>


plt.figure(figsize=(10, 5))
sns.countplot(x="Marital_Status", hue="Attrition_Flag", data=ccpredf1)
# plot to assess important variables

<AxesSubplot:xlabel='Marital_Status', ylabel='count'>


plt.figure(figsize=(10, 5))
sns.countplot(x="Income_Category", hue="Attrition_Flag", data=ccpredf1)
# plot to assess important variables

<AxesSubplot:xlabel='Income_Category', ylabel='count'>


plt.figure(figsize=(10, 5))
sns.countplot(x="Card_Category", hue="Attrition_Flag", data=ccpredf1)
# plot to assess important variables

<AxesSubplot:xlabel='Card_Category', ylabel='count'>


plt.figure(figsize=(10, 5))
sns.countplot(x="Months_on_book", hue="Attrition_Flag", data=ccpredf1)
# plot to assess important variables

<AxesSubplot:xlabel='Months_on_book', ylabel='count'>


plt.figure(figsize=(10, 5))
sns.countplot(x="Total_Relationship_Count", hue="Attrition_Flag", data=ccpredf1)
# plot to assess important variables

<AxesSubplot:xlabel='Total_Relationship_Count', ylabel='count'>


plt.figure(figsize=(10, 5))
sns.countplot(x="Months_Inactive_12_mon", hue="Attrition_Flag", data=ccpredf1)
# plot to assess important variables

<AxesSubplot:xlabel='Months_Inactive_12_mon', ylabel='count'>


plt.figure(figsize=(10, 5))
sns.countplot(x="Contacts_Count_12_mon", hue="Attrition_Flag", data=ccpredf1)
# plot to assess important variables

<AxesSubplot:xlabel='Contacts_Count_12_mon', ylabel='count'>


plt.figure(figsize=(20, 8))
sns.heatmap(ccpredf1.corr(), annot=True)  # Heatmap
plt.show()


# creating a copy of the data so that original data remains unchanged
ccpredf2 = ccpredf1.copy()


def missingvaluecheck(dat):  # function to find missing values within the dataset
    calc_total = (
        dat.isnull().sum().sort_values(ascending=False)
    )  # Checking for total null values
    calc_percent = (dat.isnull().sum() / dat.isnull().count()).sort_values(
        ascending=False
    )  # checking percentage
    data_missing = pd.concat(
        [calc_total, calc_percent], axis=1, keys=["Total", "% of Missing Val"]
    )  # to display missing data count and percentage
    return data_missing  # return the dataframe


missingvaluecheck(ccpredf2)


ccpredf2.drop(["CLIENTNUM"], axis=1, inplace=True)  # CustomerID dropped


# Replacing ABC with
ccpredf2["Income_Category"] = ccpredf2["Income_Category"].replace("abc", "Not Declared")


ccpredf3 = ccpredf2.copy()
ccpredf3.shape

(10127, 20)


ccpredf3 = pd.get_dummies(
    ccpredf3,
    columns=ccpredf3.select_dtypes(include=["object", "category"]).columns.tolist(),
    drop_first=True,
)


np.random.seed(
    1
)  # random set with seed 1, so the same set of random values can be generated everytime
ccpredf3.sample(n=10)  # Print 10 random rows to check the dataset


ccpredf3.shape

(10127, 31)


# Separating target variable and other variables
X = ccpredf3.drop(columns="Attrition_Flag_Existing Customer")
X = pd.get_dummies(X)
Y = ccpredf3["Attrition_Flag_Existing Customer"]


# Splitting data into training, validation and test set:
# first we split data into 2 parts, say temporary and test

X_temp, X_test, y_temp, y_test = train_test_split(
    X, Y, test_size=0.2, random_state=1, stratify=Y
)

# then we split the temporary set into train and validation

X_train, X_val, y_train, y_val = train_test_split(
    X_temp, y_temp, test_size=0.25, random_state=1, stratify=y_temp
)
print(X_train.shape, X_val.shape, X_test.shape)

(6075, 30) (2026, 30) (2026, 30)


imputer = SimpleImputer(strategy="median")
impute = imputer.fit(X_train)

X_train = impute.transform(X_train)
X_val = imputer.transform(X_val)
X_test = imputer.transform(X_test)


models = []  # Empty list to store all the models

# Appending models into the list
models.append(("Bagging", BaggingClassifier(random_state=1)))
models.append(("Random forest", RandomForestClassifier(random_state=1)))
models.append(("GBM", GradientBoostingClassifier(random_state=1)))
models.append(("Adaboost", AdaBoostClassifier(random_state=1)))
models.append(("Xgboost", XGBClassifier(random_state=1, eval_metric="logloss")))
models.append(("dtree", DecisionTreeClassifier(random_state=1)))

results = []  # Empty list to store all model's CV scores
names = []  # Empty list to store name of the models


# loop through all models to get the mean cross validated score
print("\n" "Cross-Validation Performance:" "\n")

for name, model in models:
    scoring = "recall"
    kfold = StratifiedKFold(
        n_splits=5, shuffle=True, random_state=1
    )  # Setting number of splits equal to 5
    cv_result = cross_val_score(
        estimator=model, X=X_train, y=y_train, scoring=scoring, cv=kfold
    )
    results.append(cv_result)
    names.append(name)
    print("{}: {}".format(name, cv_result.mean() * 100))

print("\n" "Training Performance:" "\n")

for name, model in models:
    model.fit(X_train, y_train)
    scores = recall_score(y_train, model.predict(X_train)) * 100
    print("{}: {}".format(name, scores))

Cross-Validation Performance:

Bagging: 97.52921934230022
Random forest: 98.74494410129117
GBM: 98.78412130308453
Adaboost: 97.92122226712078
Xgboost: 98.68606284515768
dtree: 96.07796955877541

Training Performance:

Bagging: 99.74504804863699
Random forest: 100.0
GBM: 99.27436752304374
Adaboost: 98.25455971759169
Xgboost: 100.0
dtree: 100.0


print("Before UpSampling, counts of label '1': {}".format(sum(y_train == 1)))
print("Before UpSampling, counts of label '0': {} \n".format(sum(y_train == 0)))

sm = SMOTE(
    sampling_strategy=1, k_neighbors=5, random_state=1
)  # Synthetic Minority Over Sampling Technique
X_train_res, y_train_res = sm.fit_resample(X_train, y_train.ravel())


print("After UpSampling, counts of label '1': {}".format(sum(y_train_res == 1)))
print("After UpSampling, counts of label '0': {} \n".format(sum(y_train_res == 0)))

Before UpSampling, counts of label '1': 5099
Before UpSampling, counts of label '0': 976 

After UpSampling, counts of label '1': 5099
After UpSampling, counts of label '0': 5099


models = []  # Empty list to store all the models

# Appending models into the list
models.append(("Bagging", BaggingClassifier(random_state=1)))
models.append(("Random forest", RandomForestClassifier(random_state=1)))
models.append(("GBM", GradientBoostingClassifier(random_state=1)))
models.append(("Adaboost", AdaBoostClassifier(random_state=1)))
models.append(("Xgboost", XGBClassifier(random_state=1, eval_metric="logloss")))
models.append(("dtree", DecisionTreeClassifier(random_state=1)))

results = []  # Empty list to store all model's CV scores
names = []  # Empty list to store name of the models


# loop through all models to get the mean cross validated score
print("\n" "Cross-Validation Performance:" "\n")

for name, model in models:
    scoring = "recall"
    kfold = StratifiedKFold(
        n_splits=5, shuffle=True, random_state=1
    )  # Setting number of splits equal to 5
    cv_result = cross_val_score(
        estimator=model, X=X_train_res, y=y_train_res, scoring=scoring, cv=kfold
    )
    results.append(cv_result)
    names.append(name)
    print("{}: {}".format(name, cv_result.mean() * 100))

print("\n" "Training Performance:" "\n")

for name, model in models:
    model.fit(X_train_res, y_train_res)
    scores = recall_score(y_train, model.predict(X_train)) * 100
    print("{}: {}".format(name, scores))

Cross-Validation Performance:

Bagging: 95.62691219765628
Random forest: 97.56830033289076
GBM: 97.68592814947372
Adaboost: 96.33256364371067
Xgboost: 98.25463256941639
dtree: 94.76393619273027

Training Performance:

Bagging: 99.52931947440675
Random forest: 100.0
GBM: 98.35261816042362
Adaboost: 96.78368307511278
Xgboost: 100.0
dtree: 100.0


from imblearn.under_sampling import ClusterCentroids


cc = ClusterCentroids()  
X_cc, y_cc = cc.fit_resample(X_train, y_train)


X_cc.shape

(1952, 30)


y_cc

0       0
1       0
2       0
3       0
4       0
       ..
1947    1
1948    1
1949    1
1950    1
1951    1
Name: Attrition_Flag_Existing Customer, Length: 1952, dtype: uint8


models = []  # Empty list to store all the models

# Appending models into the list
models.append(("Bagging", BaggingClassifier(random_state=1)))
models.append(("Random forest", RandomForestClassifier(random_state=1)))
models.append(("GBM", GradientBoostingClassifier(random_state=1)))
models.append(("Adaboost", AdaBoostClassifier(random_state=1)))
models.append(("Xgboost", XGBClassifier(random_state=1, eval_metric="logloss")))
models.append(("dtree", DecisionTreeClassifier(random_state=1)))

results = []  # Empty list to store all model's CV scores
names = []  # Empty list to store name of the models


# loop through all models to get the mean cross validated score
print("\n" "Cross-Validation Performance:" "\n")

for name, model in models:
    scoring = "recall"
    kfold = StratifiedKFold(
        n_splits=5, shuffle=True, random_state=1
    )  # Setting number of splits equal to 5
    cv_result = cross_val_score(
        estimator=model, X=X_cc, y=y_cc, scoring=scoring, cv=kfold
    )
    results.append(cv_result)
    names.append(name)
    print("{}: {}".format(name, cv_result.mean() * 100))

print("\n" "Training Performance:" "\n")

for name, model in models:
    model.fit(X_cc, y_cc)
    scores = recall_score(y_train, model.predict(X_train)) * 100
    print("{}: {}".format(name, scores))

Cross-Validation Performance:

Bagging: 95.69649398220828
Random forest: 95.90266875981162
GBM: 97.02825745682888
Adaboost: 96.10779696493982
Xgboost: 97.33647305075877
dtree: 94.46781789638933

Training Performance:

Bagging: 66.15022553441851
Random forest: 53.22612276917042
GBM: 55.42263188860561
Adaboost: 58.560502059227304
Xgboost: 58.71739556775838
dtree: 62.69856834673465


# defining a function to compute different metrics to check performance of a classification model built using sklearn
def model_performance_classification_sklearn(model, predictors, target):
    """
    Function to compute different metrics to check classification model performance

    model: classifier
    predictors: independent variables
    target: dependent variable
    """

    # predicting using the independent variables
    pred = model.predict(predictors)

    acc = accuracy_score(target, pred)  # to compute Accuracy
    recall = recall_score(target, pred)  # to compute Recall
    precision = precision_score(target, pred)  # to compute Precision
    f1 = f1_score(target, pred)  # to compute F1-score

    # creating a dataframe of metrics
    df_perf = pd.DataFrame(
        {
            "Accuracy": acc,
            "Recall": recall,
            "Precision": precision,
            "F1": f1,
        },
        index=[0],
    )

    return df_perf


def confusion_matrix_sklearn(model, predictors, target):
    """
    To plot the confusion_matrix with percentages

    model: classifier
    predictors: independent variables
    target: dependent variable
    """
    y_pred = model.predict(predictors)
    cm = confusion_matrix(target, y_pred)
    labels = np.asarray(
        [
            ["{0:0.0f}".format(item) + "\n{0:.2%}".format(item / cm.flatten().sum())]
            for item in cm.flatten()
        ]
    ).reshape(2, 2)

    plt.figure(figsize=(6, 4))
    sns.heatmap(cm, annot=labels, fmt="")
    plt.ylabel("True label")
    plt.xlabel("Predicted label")


%%time 

# defining model
model = AdaBoostClassifier(random_state=1)

# Parameter grid to pass in GridSearchCV

param_grid = {
    "n_estimators": np.arange(10, 110, 10),
    "learning_rate": [0.1, 0.01, 0.2, 0.05, 1],
    "base_estimator": [
        DecisionTreeClassifier(max_depth=1, random_state=1),
        DecisionTreeClassifier(max_depth=2, random_state=1),
        DecisionTreeClassifier(max_depth=3, random_state=1),
    ],
}

# Type of scoring used to compare parameter combinations
scorer = metrics.make_scorer(metrics.recall_score)

# Calling GridSearchCV
grid_cv = GridSearchCV(estimator=model, param_grid=param_grid, scoring=scorer, cv=5, n_jobs = -1)

# Fitting parameters in GridSeachCV
grid_cv.fit(X_train, y_train)

print(
    "Best Parameters:{} \nScore: {}".format(grid_cv.best_params_, grid_cv.best_score_)
)

Best Parameters:{'base_estimator': DecisionTreeClassifier(max_depth=1, random_state=1), 'learning_rate': 0.1, 'n_estimators': 10} 
Score: 1.0
Wall time: 1min 58s


# building model with best parameters
adb_tuned1 = AdaBoostClassifier(
    n_estimators=10,
    learning_rate=0.1,
    random_state=1,
    base_estimator=DecisionTreeClassifier(max_depth=1, random_state=1),
)

# Fit the model on training data
adb_tuned1.fit(X_train, y_train)

AdaBoostClassifier(base_estimator=DecisionTreeClassifier(max_depth=1,
                                                         random_state=1),
                   learning_rate=0.1, n_estimators=10, random_state=1)


# Calculating different metrics on train set
Adaboost_grid_train = model_performance_classification_sklearn(
    adb_tuned1, X_train, y_train
)
print("Training performance:")
Adaboost_grid_train

Training performance:


# Calculating different metrics on validation set
Adaboost_grid_val = model_performance_classification_sklearn(adb_tuned1, X_val, y_val)
print("Validation performance:")
Adaboost_grid_val

Validation performance:


# creating confusion matrix
confusion_matrix_sklearn(adb_tuned1, X_val, y_val)


%%time 

# defining model
model = AdaBoostClassifier(random_state=1)

# Parameter grid to pass in GridSearchCV

param_grid = {
    "n_estimators": np.arange(10, 110, 10),
    "learning_rate": [0.1, 0.01, 0.2, 0.05, 1],
    "base_estimator": [
        DecisionTreeClassifier(max_depth=1, random_state=1),
        DecisionTreeClassifier(max_depth=2, random_state=1),
        DecisionTreeClassifier(max_depth=3, random_state=1),
    ],
}

# Type of scoring used to compare parameter combinations
scorer = metrics.make_scorer(metrics.recall_score)

#Calling RandomizedSearchCV
randomized_cv = RandomizedSearchCV(estimator=model, param_distributions=param_grid, n_jobs = -1, n_iter=50, scoring=scorer, cv=5, random_state=1)

#Fitting parameters in RandomizedSearchCV
randomized_cv.fit(X_train,y_train)

print("Best parameters are {} with CV score={}:" .format(randomized_cv.best_params_,randomized_cv.best_score_))

Best parameters are {'n_estimators': 50, 'learning_rate': 0.01, 'base_estimator': DecisionTreeClassifier(max_depth=1, random_state=1)} with CV score=1.0:
Wall time: 43 s


# building model with best parameters
adb_tuned2 = AdaBoostClassifier(
    n_estimators=50,
    learning_rate=0.01,
    random_state=1,
    base_estimator=DecisionTreeClassifier(max_depth=1, random_state=1),
)

# Fit the model on training data
adb_tuned2.fit(X_train, y_train)

AdaBoostClassifier(base_estimator=DecisionTreeClassifier(max_depth=1,
                                                         random_state=1),
                   learning_rate=0.01, random_state=1)


# Calculating different metrics on train set
Adaboost_random_train = model_performance_classification_sklearn(
    adb_tuned2, X_train, y_train
)
print("Training performance:")
Adaboost_random_train

Training performance:


# Calculating different metrics on validation set
Adaboost_random_val = model_performance_classification_sklearn(adb_tuned2, X_val, y_val)
print("Validation performance:")
Adaboost_random_val

Validation performance:


# creating confusion matrix
confusion_matrix_sklearn(adb_tuned2, X_val, y_val)


%%time 

#defining model
model = XGBClassifier(random_state=1,eval_metric='logloss')

#Parameter grid to pass in GridSearchCV
param_grid={'n_estimators':np.arange(50,150,50),
            'scale_pos_weight':[2,5,10],
            'learning_rate':[0.01,0.1,0.2,0.05],
            'gamma':[0,1,3,5],
            'subsample':[0.8,0.9,1],
            'max_depth':np.arange(1,5,1),
            'reg_lambda':[5,10]}


# Type of scoring used to compare parameter combinations
scorer = metrics.make_scorer(metrics.recall_score)

#Calling GridSearchCV
grid_cv = GridSearchCV(estimator=model, param_grid=param_grid, scoring=scorer, cv=5, n_jobs = -1, verbose= 2)

#Fitting parameters in GridSeachCV
grid_cv.fit(X_train,y_train)


print("Best parameters are {} with CV score={}:" .format(grid_cv.best_params_,grid_cv.best_score_))

Fitting 5 folds for each of 2304 candidates, totalling 11520 fits
Best parameters are {'gamma': 0, 'learning_rate': 0.01, 'max_depth': 1, 'n_estimators': 50, 'reg_lambda': 5, 'scale_pos_weight': 2, 'subsample': 0.8} with CV score=1.0:
Wall time: 20min 8s


# building model with best parameters
xgb_tuned1 = XGBClassifier(
    random_state=1,
    n_estimators=50,
    scale_pos_weight=2,
    subsample=0.8,
    learning_rate=0.01,
    gamma=0,
    eval_metric="logloss",
    reg_lambda=5,
    max_depth=1,
)

# Fit the model on training data
xgb_tuned1.fit(X_train, y_train)

XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,
              colsample_bynode=1, colsample_bytree=1, eval_metric='logloss',
              gamma=0, gpu_id=-1, importance_type='gain',
              interaction_constraints='', learning_rate=0.01, max_delta_step=0,
              max_depth=1, min_child_weight=1, missing=nan,
              monotone_constraints='()', n_estimators=50, n_jobs=8,
              num_parallel_tree=1, random_state=1, reg_alpha=0, reg_lambda=5,
              scale_pos_weight=2, subsample=0.8, tree_method='exact',
              validate_parameters=1, verbosity=None)


# Calculating different metrics on train set
xgboost_grid_train = model_performance_classification_sklearn(
    xgb_tuned1, X_train, y_train
)
print("Training performance:")
xgboost_grid_train

Training performance:


# Calculating different metrics on validation set
xgboost_grid_val = model_performance_classification_sklearn(xgb_tuned1, X_val, y_val)
print("Validation performance:")
xgboost_grid_val

Validation performance:


# creating confusion matrix
confusion_matrix_sklearn(xgb_tuned1, X_val, y_val)


%%time

# defining model
model = XGBClassifier(random_state=1,eval_metric='logloss')

# Parameter grid to pass in RandomizedSearchCV
param_grid={'n_estimators':np.arange(50,150,50),
            'scale_pos_weight':[2,5,10],
            'learning_rate':[0.01,0.1,0.2,0.05],
            'gamma':[0,1,3,5],
            'subsample':[0.8,0.9,1],
            'max_depth':np.arange(1,5,1),
            'reg_lambda':[5,10]}

# Type of scoring used to compare parameter combinations
scorer = metrics.make_scorer(metrics.recall_score)

#Calling RandomizedSearchCV
xgb_tuned2 = RandomizedSearchCV(estimator=model, param_distributions=param_grid, n_iter=50, scoring=scorer, cv=5, random_state=1, n_jobs = -1)

#Fitting parameters in RandomizedSearchCV
xgb_tuned2.fit(X_train,y_train)

print("Best parameters are {} with CV score={}:" .format(xgb_tuned2.best_params_,xgb_tuned2.best_score_))

Best parameters are {'subsample': 0.8, 'scale_pos_weight': 2, 'reg_lambda': 10, 'n_estimators': 100, 'max_depth': 1, 'learning_rate': 0.01, 'gamma': 5} with CV score=1.0:
Wall time: 25.9 s


# building model with best parameters
xgb_tuned2 = XGBClassifier(
    random_state=1,
    n_estimators=100,
    scale_pos_weight=2,
    gamma=5,
    subsample=0.8,
    learning_rate=0.1,
    eval_metric="logloss",
    max_depth=1,
    reg_lambda=10,
)
# Fit the model on training data
xgb_tuned2.fit(X_train, y_train)

XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,
              colsample_bynode=1, colsample_bytree=1, eval_metric='logloss',
              gamma=5, gpu_id=-1, importance_type='gain',
              interaction_constraints='', learning_rate=0.1, max_delta_step=0,
              max_depth=1, min_child_weight=1, missing=nan,
              monotone_constraints='()', n_estimators=100, n_jobs=8,
              num_parallel_tree=1, random_state=1, reg_alpha=0, reg_lambda=10,
              scale_pos_weight=2, subsample=0.8, tree_method='exact',
              validate_parameters=1, verbosity=None)


# Calculating different metrics on train set
xgboost_random_train = model_performance_classification_sklearn(
    xgb_tuned2, X_train, y_train
)
print("Training performance:")
xgboost_random_train

Training performance:


# Calculating different metrics on validation set
xgboost_random_val = model_performance_classification_sklearn(xgb_tuned2, X_val, y_val)
print("Validation performance:")
xgboost_random_val

Validation performance:


# creating confusion matrix
confusion_matrix_sklearn(xgb_tuned2, X_val, y_val)


# training performance comparison

models_train_comp_df = pd.concat(
    [
        Adaboost_grid_train.T,
        Adaboost_random_train.T,
        xgboost_grid_train.T,
        xgboost_random_train.T,
    ],
    axis=1,
)
models_train_comp_df.columns = [
    "AdaBoost Tuned with Grid search",
    "AdaBoost Tuned with Random search",
    "Xgboost Tuned with Grid search",
    "Xgboost Tuned with Random Search",
]
print("Training performance comparison:")
models_train_comp_df

Training performance comparison:


# Validation performance comparison

models_val_comp_df = pd.concat(
    [
        Adaboost_grid_val.T,
        Adaboost_random_val.T,
        xgboost_grid_val.T,
        xgboost_random_val.T,
    ],
    axis=1,
)
models_val_comp_df.columns = [
    "AdaBoost Tuned with Grid search",
    "AdaBoost Tuned with Random search",
    "Xgboost Tuned with Grid search",
    "Xgboost Tuned with Random Search",
]
print("Validation performance comparison:")
models_val_comp_df

Validation performance comparison:


# Calculating different metrics on the test set
xgboost_rand_test = model_performance_classification_sklearn(xgb_tuned2, X_test, y_test)
print("Test performance:")
xgboost_rand_test

Test performance:


feature_names = X.columns
importances = xgb_tuned2.feature_importances_
indices = np.argsort(importances)

plt.figure(figsize=(12, 12))
plt.title("Feature Importances")
plt.barh(range(len(indices)), importances[indices], color="blue", align="center")
plt.yticks(range(len(indices)), [feature_names[i] for i in indices])
plt.xlabel("Relative Importance")
plt.show()


# Separating target variable and other variables
X = ccpredf3.drop(columns="Attrition_Flag_Existing Customer")
Y = ccpredf3["Attrition_Flag_Existing Customer"]


# Splitting the data into train and test sets
X_train, X_test, y_train, y_test = train_test_split(
    X, Y, test_size=0.30, random_state=1, stratify=Y
)
print(X_train.shape, X_test.shape)

(7088, 30) (3039, 30)


from sklearn.pipeline import Pipeline


# Creating new pipeline with best parameters
model = Pipeline(steps=[("ADA",XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,
              colsample_bynode=1, colsample_bytree=1, eval_metric='logloss',
              gamma=5, gpu_id=-1, importance_type='gain',
              interaction_constraints='', learning_rate=0.1, max_delta_step=0,
              max_depth=1, min_child_weight=1,
              monotone_constraints='()', n_estimators=100, n_jobs=8,
              num_parallel_tree=1, random_state=1, reg_alpha=0, reg_lambda=10,
              scale_pos_weight=2, subsample=0.8, tree_method='exact',
              validate_parameters=1, verbosity=None),
),])
# Fit the model on training data
model.fit(X_train, y_train)

Pipeline(steps=[('ADA',
                 XGBClassifier(base_score=0.5, booster='gbtree',
                               colsample_bylevel=1, colsample_bynode=1,
                               colsample_bytree=1, eval_metric='logloss',
                               gamma=5, gpu_id=-1, importance_type='gain',
                               interaction_constraints='', learning_rate=0.1,
                               max_delta_step=0, max_depth=1,
                               min_child_weight=1, missing=nan,
                               monotone_constraints='()', n_estimators=100,
                               n_jobs=8, num_parallel_tree=1, random_state=1,
                               reg_alpha=0, reg_lambda=10, scale_pos_weight=2,
                               subsample=0.8, tree_method='exact',
                               validate_parameters=1, verbosity=None))])

	CLIENTNUM	Attrition_Flag	Customer_Age	Gender	Dependent_count	Education_Level	Marital_Status	Income_Category	Card_Category	Months_on_book	Total_Relationship_Count	Months_Inactive_12_mon	Contacts_Count_12_mon	Credit_Limit	Total_Revolving_Bal	Avg_Open_To_Buy	Total_Amt_Chng_Q4_Q1	Total_Trans_Amt	Total_Trans_Ct	Total_Ct_Chng_Q4_Q1	Avg_Utilization_Ratio
0	768805383	Existing Customer	45	M	3	High School	Married	$60K - $80K	Blue	39	5	1	3	12691.00000	777	11914.00000	1.33500	1144	42	1.62500	0.06100
1	818770008	Existing Customer	49	F	5	Graduate	Single	Less than $40K	Blue	44	6	1	2	8256.00000	864	7392.00000	1.54100	1291	33	3.71400	0.10500
2	713982108	Existing Customer	51	M	3	Graduate	Married	$80K - $120K	Blue	36	4	1	0	3418.00000	0	3418.00000	2.59400	1887	20	2.33300	0.00000
3	769911858	Existing Customer	40	F	4	High School	NaN	Less than $40K	Blue	34	3	4	1	3313.00000	2517	796.00000	1.40500	1171	20	2.33300	0.76000
4	709106358	Existing Customer	40	M	3	Uneducated	Married	$60K - $80K	Blue	21	5	1	0	4716.00000	0	4716.00000	2.17500	816	28	2.50000	0.00000
5	713061558	Existing Customer	44	M	2	Graduate	Married	$40K - $60K	Blue	36	3	1	2	4010.00000	1247	2763.00000	1.37600	1088	24	0.84600	0.31100
6	810347208	Existing Customer	51	M	4	NaN	Married	$120K +	Gold	46	6	1	3	34516.00000	2264	32252.00000	1.97500	1330	31	0.72200	0.06600
7	818906208	Existing Customer	32	M	0	High School	NaN	$60K - $80K	Silver	27	2	2	2	29081.00000	1396	27685.00000	2.20400	1538	36	0.71400	0.04800
8	710930508	Existing Customer	37	M	3	Uneducated	Single	$60K - $80K	Blue	36	5	2	0	22352.00000	2517	19835.00000	3.35500	1350	24	1.18200	0.11300
9	719661558	Existing Customer	48	M	2	Graduate	Single	$80K - $120K	Blue	36	6	3	3	11656.00000	1677	9979.00000	1.52400	1441	32	0.88200	0.14400

	CLIENTNUM	Attrition_Flag	Customer_Age	Gender	Dependent_count	Education_Level	Marital_Status	Income_Category	Card_Category	Months_on_book	Total_Relationship_Count	Months_Inactive_12_mon	Contacts_Count_12_mon	Credit_Limit	Total_Revolving_Bal	Avg_Open_To_Buy	Total_Amt_Chng_Q4_Q1	Total_Trans_Amt	Total_Trans_Ct	Total_Ct_Chng_Q4_Q1	Avg_Utilization_Ratio
6498	712389108	Existing Customer	43	F	2	Graduate	Married	Less than $40K	Blue	36	6	3	2	2570.00000	2107	463.00000	0.65100	4058	83	0.76600	0.82000
9013	718388733	Existing Customer	38	F	1	College	NaN	Less than $40K	Blue	32	2	3	3	2609.00000	1259	1350.00000	0.87100	8677	96	0.62700	0.48300
2053	710109633	Existing Customer	39	M	2	College	Married	$60K - $80K	Blue	31	6	3	2	9871.00000	1061	8810.00000	0.54500	1683	34	0.47800	0.10700
3211	717331758	Existing Customer	44	M	4	Graduate	Married	$120K +	Blue	32	6	3	4	34516.00000	2517	31999.00000	0.76500	4228	83	0.59600	0.07300
5559	709460883	Attrited Customer	38	F	2	Doctorate	Married	Less than $40K	Blue	28	5	2	4	1614.00000	0	1614.00000	0.60900	2437	46	0.43800	0.00000
6106	789105183	Existing Customer	54	M	3	Post-Graduate	Single	$80K - $120K	Silver	42	3	1	2	34516.00000	2488	32028.00000	0.55200	4401	87	0.77600	0.07200
4150	771342183	Attrited Customer	53	F	3	Graduate	Single	$40K - $60K	Blue	40	6	3	2	1625.00000	0	1625.00000	0.68900	2314	43	0.43300	0.00000
2205	708174708	Existing Customer	38	M	4	Graduate	Married	$40K - $60K	Blue	27	6	2	4	5535.00000	1276	4259.00000	0.63600	1764	38	0.90000	0.23100
4145	718076733	Existing Customer	43	M	1	Graduate	Single	$60K - $80K	Silver	31	4	3	3	25824.00000	1170	24654.00000	0.68400	3101	73	0.78000	0.04500
5324	821889858	Attrited Customer	50	F	1	Doctorate	Single	abc	Blue	46	6	4	3	1970.00000	1477	493.00000	0.66200	2493	44	0.57100	0.75000
7671	788885133	Existing Customer	57	F	3	College	NaN	Less than $40K	Silver	42	6	1	1	12675.00000	2422	10253.00000	0.64200	3912	63	0.57500	0.19100
2903	717427983	Existing Customer	50	F	3	High School	Single	Less than $40K	Silver	39	4	3	4	11176.00000	2422	8754.00000	1.00300	2682	64	0.64100	0.21700
9924	827318958	Existing Customer	49	F	4	NaN	Single	Less than $40K	Blue	45	5	3	2	9431.00000	1785	7646.00000	0.60300	14261	99	0.65000	0.18900
9251	708863733	Existing Customer	35	M	3	NaN	Single	$40K - $60K	Gold	19	1	1	3	22725.00000	808	21917.00000	0.77900	13847	120	0.87500	0.03600
2675	824667708	Existing Customer	32	F	1	Graduate	Single	Less than $40K	Blue	28	6	3	3	2428.00000	1027	1401.00000	0.82400	3007	84	0.86700	0.42300
7381	798281808	Existing Customer	48	M	4	High School	Married	$60K - $80K	Blue	42	5	4	3	3879.00000	1214	2665.00000	0.92400	5158	79	0.68100	0.31300
8660	720849708	Existing Customer	44	M	4	Graduate	Single	$80K - $120K	Blue	37	3	3	1	14388.00000	2517	11871.00000	0.70900	7042	85	0.63500	0.17500
7173	710157108	Existing Customer	42	F	3	Graduate	Single	$40K - $60K	Blue	33	6	2	3	3376.00000	1661	1715.00000	0.61800	4629	75	0.92300	0.49200
2844	720016308	Existing Customer	62	F	1	NaN	Single	Less than $40K	Blue	54	4	2	2	1927.00000	1319	608.00000	0.67200	4199	77	0.75000	0.68400
4427	816864483	Existing Customer	39	F	4	Uneducated	Single	$40K - $60K	Blue	34	4	2	2	10264.00000	803	9461.00000	0.80200	4003	74	0.72100	0.07800

	count	unique	top	freq	mean	std	min	25%	50%	75%	max
CLIENTNUM	10127.00000	NaN	NaN	NaN	739177606.33366	36903783.45023	708082083.00000	713036770.50000	717926358.00000	773143533.00000	828343083.00000
Attrition_Flag	10127	2	Existing Customer	8500	NaN	NaN	NaN	NaN	NaN	NaN	NaN
Customer_Age	10127.00000	NaN	NaN	NaN	46.32596	8.01681	26.00000	41.00000	46.00000	52.00000	73.00000
Gender	10127	2	F	5358	NaN	NaN	NaN	NaN	NaN	NaN	NaN
Dependent_count	10127.00000	NaN	NaN	NaN	2.34620	1.29891	0.00000	1.00000	2.00000	3.00000	5.00000
Education_Level	8608	6	Graduate	3128	NaN	NaN	NaN	NaN	NaN	NaN	NaN
Marital_Status	9378	3	Married	4687	NaN	NaN	NaN	NaN	NaN	NaN	NaN
Income_Category	10127	6	Less than $40K	3561	NaN	NaN	NaN	NaN	NaN	NaN	NaN
Card_Category	10127	4	Blue	9436	NaN	NaN	NaN	NaN	NaN	NaN	NaN
Months_on_book	10127.00000	NaN	NaN	NaN	35.92841	7.98642	13.00000	31.00000	36.00000	40.00000	56.00000
Total_Relationship_Count	10127.00000	NaN	NaN	NaN	3.81258	1.55441	1.00000	3.00000	4.00000	5.00000	6.00000
Months_Inactive_12_mon	10127.00000	NaN	NaN	NaN	2.34117	1.01062	0.00000	2.00000	2.00000	3.00000	6.00000
Contacts_Count_12_mon	10127.00000	NaN	NaN	NaN	2.45532	1.10623	0.00000	2.00000	2.00000	3.00000	6.00000
Credit_Limit	10127.00000	NaN	NaN	NaN	8631.95370	9088.77665	1438.30000	2555.00000	4549.00000	11067.50000	34516.00000
Total_Revolving_Bal	10127.00000	NaN	NaN	NaN	1162.81406	814.98734	0.00000	359.00000	1276.00000	1784.00000	2517.00000
Avg_Open_To_Buy	10127.00000	NaN	NaN	NaN	7469.13964	9090.68532	3.00000	1324.50000	3474.00000	9859.00000	34516.00000
Total_Amt_Chng_Q4_Q1	10127.00000	NaN	NaN	NaN	0.75994	0.21921	0.00000	0.63100	0.73600	0.85900	3.39700
Total_Trans_Amt	10127.00000	NaN	NaN	NaN	4404.08630	3397.12925	510.00000	2155.50000	3899.00000	4741.00000	18484.00000
Total_Trans_Ct	10127.00000	NaN	NaN	NaN	64.85869	23.47257	10.00000	45.00000	67.00000	81.00000	139.00000
Total_Ct_Chng_Q4_Q1	10127.00000	NaN	NaN	NaN	0.71222	0.23809	0.00000	0.58200	0.70200	0.81800	3.71400
Avg_Utilization_Ratio	10127.00000	NaN	NaN	NaN	0.27489	0.27569	0.00000	0.02300	0.17600	0.50300	0.99900

	Customer_Age	Dependent_count	Months_on_book	Total_Relationship_Count	Months_Inactive_12_mon	Contacts_Count_12_mon	Credit_Limit	Total_Revolving_Bal	Avg_Open_To_Buy	Total_Amt_Chng_Q4_Q1	Total_Trans_Amt	Total_Trans_Ct	Total_Ct_Chng_Q4_Q1	Avg_Utilization_Ratio	Attrition_Flag_Existing Customer	Gender_M	Education_Level_Doctorate	Education_Level_Graduate	Education_Level_Post-Graduate	Marital_Status_Married	Marital_Status_Single	Income_Category_$40K - $60K	Income_Category_$60K - $80K	Income_Category_$80K - $120K	Income_Category_Less than $40K	Income_Category_Not Declared	Card_Category_Silver
6498	43	2	36	6	3	2	2570.00000	2107	463.00000	0.65100	4058	83	0.76600	0.82000	1	0	0	1	0	1	0	0	0	0	1	0	0
9013	38	1	32	2	3	3	2609.00000	1259	1350.00000	0.87100	8677	96	0.62700	0.48300	1	0	0	0	0	0	0	0	0	0	1	0	0
2053	39	2	31	6	3	2	9871.00000	1061	8810.00000	0.54500	1683	34	0.47800	0.10700	1	1	0	0	0	1	0	0	1	0	0	0	0
3211	44	4	32	6	3	4	34516.00000	2517	31999.00000	0.76500	4228	83	0.59600	0.07300	1	1	0	1	0	1	0	0	0	0	0	0	0
5559	38	2	28	5	2	4	1614.00000	0	1614.00000	0.60900	2437	46	0.43800	0.00000	0	0	1	0	0	1	0	0	0	0	1	0	0
6106	54	3	42	3	1	2	34516.00000	2488	32028.00000	0.55200	4401	87	0.77600	0.07200	1	1	0	0	1	0	1	0	0	1	0	0	1
4150	53	3	40	6	3	2	1625.00000	0	1625.00000	0.68900	2314	43	0.43300	0.00000	0	0	0	1	0	0	1	1	0	0	0	0	0
2205	38	4	27	6	2	4	5535.00000	1276	4259.00000	0.63600	1764	38	0.90000	0.23100	1	1	0	1	0	1	0	1	0	0	0	0	0
4145	43	1	31	4	3	3	25824.00000	1170	24654.00000	0.68400	3101	73	0.78000	0.04500	1	1	0	1	0	0	1	0	1	0	0	0	1
5324	50	1	46	6	4	3	1970.00000	1477	493.00000	0.66200	2493	44	0.57100	0.75000	0	0	1	0	0	0	1	0	0	0	0	1	0

	AdaBoost Tuned with Grid search	AdaBoost Tuned with Random search	Xgboost Tuned with Grid search	Xgboost Tuned with Random Search
Accuracy	0.83934	0.83934	0.83934	0.89333
Recall	1.00000	1.00000	1.00000	0.99686
Precision	0.83934	0.83934	0.83934	0.88941
F1	0.91265	0.91265	0.91265	0.94008

Credit Card Users Churn Prediction¶

Background¶

Objective:¶

Data Context¶

Customer details:¶

Importing all the required packages for the project:¶

Read in the dataset using pandas¶

To assess the data set (Top 10)¶

Check the shape of the provided data¶

Check datatype count¶

Get the complete info of the dataset¶

Checking for missing values in the data¶

Check for duplicate data¶

Describe all columns¶

EDA¶

Data Analysis: Univariate Analysis¶

Data Analysis: Bivariate Analysis¶

Insights based on EDA¶

Data Pre-Processing¶

Missing Value Treatment¶

Building the model (using KFold and cross_val_score)¶

Model building Oversampled data¶

Model building Undersampled data¶

Hyperparameter Tuning¶

Hyperparameter tuning using AdaBoost Classifier¶

Adaboost¶

GridSearchCV¶

RandomizedSearchCV¶

XGBoost¶

GridSearchCV¶

RandomizedSearchCV¶

Comparing all models¶

Performance on the test set¶

Create Model Using Pipeline¶

Business Recommendations¶

	Total	% of Missing Val
Education_Level	1519	0.15000
Marital_Status	749	0.07396
Avg_Utilization_Ratio	0	0.00000
Months_on_book	0	0.00000
Attrition_Flag	0	0.00000
Customer_Age	0	0.00000
Gender	0	0.00000
Dependent_count	0	0.00000
Income_Category	0	0.00000
Card_Category	0	0.00000
Total_Relationship_Count	0	0.00000
Total_Ct_Chng_Q4_Q1	0	0.00000
Months_Inactive_12_mon	0	0.00000
Contacts_Count_12_mon	0	0.00000
Credit_Limit	0	0.00000
Total_Revolving_Bal	0	0.00000
Avg_Open_To_Buy	0	0.00000
Total_Amt_Chng_Q4_Q1	0	0.00000
Total_Trans_Amt	0	0.00000
Total_Trans_Ct	0	0.00000
CLIENTNUM	0	0.00000

	AdaBoost Tuned with Grid search	AdaBoost Tuned with Random search	Xgboost Tuned with Grid search	Xgboost Tuned with Random Search
Accuracy	0.83909	0.83909	0.83909	0.88648
Recall	1.00000	1.00000	1.00000	0.99882
Precision	0.83909	0.83909	0.83909	0.88162
F1	0.91251	0.91251	0.91251	0.93657